Search

We examine the effect of streaming based on ability levels on individuals' civic participation throughout their adult life. The hypothesis we test is that ability grouping influences individuals' general self-concept and, consequently, their civic participation choices across the life course. We employ data from the British National Child Development Study, which follows all UK citizens born during a certain week in 1958. Six binary variables observed at 33, 42, and 51 years of age are considered to measure civic participation. Our approach defines causal estimands with multiple treatments referring to the evolution of civic engagement over time in terms of potential versions of a sequence of latent variables assumed to follow a Markov chain with initial and transition probabilities depending on posttreatment time-varying covariates. The model also addresses partially or entirely missing data on one or more indicators at a given time occasion and missing posttreatment covariate values using dummy indicators. The model is estimated by maximizing a weighted log-likelihood function with weights corresponding to the inverse probability of the received treatment obtained from a multinomial logit model based on pretreatment covariates. Our results show that ability grouping affects the civic participation of high-ability individuals when they are 33 years old with respect to participation in general elections. [ABSTRACT FROM AUTHOR]

We study how early-life cognitive skills, non-cognitive abilities, and family characteristics influence educational choices and affect later employment outcomes and wages. The analysis was carried out on a cohort of UK females observed at different life stages by adopting the British National Child Development Study database. Our findings provide evidence of how early-life abilities and family characteristics affect both the educational attainment and later labour market outcomes of female workers. However, we found that educational levels interact with early-life abilities, productive characteristics in general, and other characteristics, giving rise to different employment outcomes and income prospects conditioned on educational attainment. Occupational outcomes and wages of low-educated women are more sensitive to factors that are not strictly linked to productivity. [ABSTRACT FROM AUTHOR]

Background The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19–affected UK population in terms of geographic, demographic, and temporal coverage. Findings The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g. sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. Conclusion The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models. [ABSTRACT FROM AUTHOR]

Background: Evidence provides inconsistent findings on risk factors and health outcomes associated with loneliness. The aim of this work was to grade the evidence on risk factors and health outcomes associated with loneliness, using an umbrella review approach.Methods: For each meta-analytic association, random-effects summary effect size, 95% confidence intervals (CIs), heterogeneity, evidence for small-study effect, excess significance bias and 95% prediction intervals were calculated, and used to grade significant evidence (p<0.05) from convincing to weak. For narrative systematic reviews, findings were reported descriptively.Results: From 210 studies initially evaluated, 14 publications were included, reporting on 18 outcomes, 795 studies, and 746,706 participants. Highly suggestive evidence (class II) supported the association between loneliness and incident dementia (relative risk, RR=1.26; 95%CI: 1.14-1.40, I2 23.6%), prevalent paranoia (odds ratio, OR=3.36; 95%CI: 2.51-4.49, I2 92.8%) and prevalent psychotic symptoms (OR=2.33; 95%CI: 1.68-3.22, I2 56.5%). Pooled data supported the longitudinal association between loneliness and suicide attempts and depressive symptoms. In narrative systematic reviews, factors cross-sectionally associated with loneliness were age (in a U-shape way), female sex, quality of social contacts, low competence, socio-economic status and medical chronic conditions.Limitations: Low quality of the studies included; mainly cross-sectional evidence.Conclusions: This work is the first meta-evidence synthesis showing that highly suggestive and significant evidence supports the association between loneliness and adverse mental and physical health outcomes. More cohort studies are needed to disentangle the direction of the association between risk factors for loneliness and its related health outcomes. [ABSTRACT FROM AUTHOR]

Competing Interests: Conflict of interest: J. Jacob reports fees from Boehringer Ingelheim and Roche unrelated to the current submission and is supported by a Clinical Research Career Development Fellowship 209553/Z/17/Z from the Wellcome Trust, and by the NIHR Biomedical Research Centre at University College London. Conflict of interest: D. Alexander has nothing to disclose. Conflict of interest: J.K. Baillie reports grants from DHSC National Institute of Health Research UK, Medical Research Council UK, Wellcome Trust, Fiona Elizabeth Agnew Trust, Intensive Care Society and Chief Scientist Office, during the conduct of the study. Conflict of interest: R. Berka is an Associate at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: O. Bertolli is an Associate at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: J. Blackwood has nothing to disclose. Conflict of interest: I. Buchan reports personal fees from and acting as advisor for AstraZeneca, grants from NIHR, and is a former employee of Microsoft, outside the submitted work. Conflict of interest: C. Bloomfield reports that NCIMI is funded through support from the Industry Strategy Challenge Fund, by Innovate UK grant 104688. Conflict of interest: D. Cushnan has nothing to disclose. Conflict of interest: A. Docherty has nothing to disclose. Conflict of interest: A. Edey has nothing to disclose. Conflict of interest: A. Favaro is a Lead Data Scientist at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: F. Gleeson has nothing to disclose. Conflict of interest: M. Halling-Brown is an advisor to Google Health on the MAMMOTH project, and has a Visiting Professorship with University of Surrey as part of a research collaboration with Transpara. Conflict of interest: S. Hare has nothing to disclose. Conflict of interest: E. Jefferson reports grants from Medical Research Council (MRC), Health Data Research (HDR) UK, National Institute of Health Research (NIHR), Chief Scientist Office (CSO), Engineering and Physical Sciences Research Council (EPSRC), Health Foundation, Data Lab, Scottish Government, NHS Fife Health Board and EU Horizon 2020, during the conduct of the study. Conflict of interest: A. Johnstone has nothing to disclose. Conflict of interest: M. Kirby is a Principal at Faculty; Faculty has a paid partnership with NHSX to build its new AI lab to help drive digital transformation and the use of AI in the NHS. This contract has been extended for setting up the platform environment that manages the data storage for the National COVID-19 Chest Imaging Database (NCCID); the platform is being made available by Faculty at zero licence cost. In addition, Faculty is contracted to support NHS England and Improvement as well as NHSX with its data response strategy to COVID-19, which includes developing dashboards, models and simulations to provide information to central government decision-makers. Conflict of interest: R. McStay has nothing to disclose. Conflict of interest: A. Nair reports salary reimbursement from Biomedical Research Centre UCL, and is medical advisor to Aidence BV, outside the submitted work. Conflict of interest: P.J.M. Openshaw reports personal fees for consultancy work with Janssen, J and J and Sanofi; grants from MRC, European Union, NIHR Biomedical Research Centre and Wellcome Trust, collaborative grants with GSK, personal fees from the European Respiratory Society and an NIHR Senior Investigator Award outside the submitted work; in addition, P.J.M. Openshaw was President of the British Society for Immunology; this was an unpaid appointment but travel and accommodation at some meetings was provided by the Society. Conflict of interest: G. Parker is director and shareholder of Bioxydyn Limited, outside the submitted work. Conflict of interest: G. Reilly has nothing to disclose. Conflict of interest: G. Robinson has nothing to disclose. Conflict of interest: G. Roditi has nothing to disclose. Conflict of interest: J.C.L. Rodrigues has nothing to disclose. Conflict of interest: N. Sebire has nothing to disclose. Conflict of interest: M.G. Semple reports grants from DHSC National Institute of Health Research UK, Medical Research Council UK, Health Protection Research Unit in Emerging and Zoonotic Infections, and University of Liverpool, during the conduct of the study; and is minority owner of Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work. Conflict of interest: C. Sudlow has nothing to disclose. Conflict of interest: N. Woznitza reports grants from Cancer Research UK and Roy Castle Lung Cancer Foundation, personal fees from InHealth, outside the submitted work. Conflict of interest: I. Joshi has nothing to disclose.